CN112835541A - Printing method, device and equipment for identifying type of 3D model and storage medium - Google Patents
Printing method, device and equipment for identifying type of 3D model and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a printing method, a printing device, printing equipment and a storage medium for identifying a 3D model type, wherein the method comprises the following steps: acquiring an imported 3D model; acquiring a plurality of screenshot photos according to the 3D model; inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type; and acquiring corresponding parameter configuration according to the matched 3D model type and printing. According to the printing method for identifying the type of the 3D model, the type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting needs to be carried out through manual judgment in the prior art is solved, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are achieved.
Description
Technical Field
The embodiment of the invention relates to a 3D technology, in particular to a printing method, a printing device, printing equipment and a storage medium for identifying a 3D model type.
Background
In the existing printing mode, a user designs a 3D model, and different printing parameters are set according to the characteristics of the model; and slicing the model, generating a corresponding printing file, and printing. Different parameters are required to be set for printing of different models, the setting of the parameters is relatively complex, certain practical experience is required, and many parameters need to be manually identified according to experience. Such as: the manual support is added, and some suspended places need to be supported to finish printing, but the parameters cannot be set according to a universal rule and can only be set according to the type, the shape and some use experiences of the model, so that a beginner has difficulty in thinking of printing the high-quality model.
Disclosure of Invention
The invention provides a printing method, a printing device, printing equipment and a storage medium for identifying a 3D model type, so as to realize the effects of automatically identifying the model, thereby improving the printing precision and simplifying the printing process.
In a first aspect, an embodiment of the present invention provides a printing method for identifying a type of a 3D model, including:
acquiring an imported 3D model;
acquiring a plurality of screenshot photos according to the 3D model;
inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and acquiring corresponding parameter configuration according to the matched 3D model type and printing.
The type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and the relevant settings are automatically matched, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are realized.
Optionally, the obtaining of the imported 3D model further includes:
acquiring parameter configurations of a plurality of models matched with the plurality of models;
and establishing a database according to the multiple models and the parameter configuration matched with the multiple models.
By establishing the database, historical data can be conveniently stored and stored, and the model and the corresponding parameter configuration can be conveniently and rapidly compared and inquired.
Optionally, the obtaining of the multiple screenshot photos according to the 3D model includes:
and shooting from multiple directions according to the 3D model to obtain multiple screenshot pictures.
Optionally, the taking from multiple directions according to the 3D model to obtain multiple screenshot photos includes:
and shooting from the front, the side and the back according to the 3D model to obtain a plurality of screenshot photos.
The 3D model is shot in multiple directions, so that the deep learning model can accurately identify the type of the 3D model conveniently.
Optionally, after obtaining the plurality of screenshot photos according to the 3D model type, the method further includes:
and training the untrained deep learning model to obtain the trained deep learning model.
The training of the untrained deep learning model to obtain the trained deep learning model comprises:
optionally, a 3D model type matching the sample screenshot picture and the sample screenshot picture is obtained;
and training the untrained deep learning model according to the sample screenshot picture and the 3D model type matched with the sample screenshot picture to obtain the trained deep learning model.
And training the untrained deep learning model through the sample picture and the matched 3D model so as to improve the recognition precision and the recognition accuracy of the deep learning model.
Optionally, the obtaining and printing the corresponding parameter configuration according to the matched 3D model includes:
acquiring corresponding parameter configuration according to the matched 3D model and setting;
and carrying out slice printing according to the set 3D model.
Slice printing is performed through the 3D model, so that a high-precision printing model can be obtained conveniently.
In a second aspect, an embodiment of the present invention further provides a printing apparatus for identifying a type of a 3D model, where the apparatus includes:
the model acquisition module is used for acquiring the imported 3D model;
the picture acquisition module is used for acquiring a plurality of screenshot pictures according to the 3D model;
the model matching module is used for inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and the model printing module is used for acquiring corresponding parameter configuration according to the matched 3D model type and printing.
In a third aspect, an embodiment of the present invention further provides a printing apparatus for identifying a type of a 3D model, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a printing method for identifying a type of a 3D model as described in any one of the above.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the printing method for identifying the type of the 3D model as described in any one of the above.
The printing method for identifying the type of the 3D model provided by the embodiment of the invention comprises the following steps: obtaining an imported 3D model; acquiring a plurality of screenshot photos according to the 3D model; inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type; and acquiring corresponding parameter configuration according to the matched 3D model type and printing. The type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting is carried out through manual judgment in the prior art is solved, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are achieved.
Drawings
FIG. 1 is a schematic flow chart illustrating a printing method for identifying a type of a 3D model according to a first embodiment of the present invention;
FIG. 2 is a schematic flow chart of a printing method for identifying a type of a 3D model according to a second embodiment of the present invention;
FIG. 3 is a schematic flow chart of another printing method for identifying a 3D model type according to a second embodiment of the present invention;
FIG. 4 is a schematic flow chart of another printing method for identifying a 3D model type according to a second embodiment of the present invention;
FIG. 5 is a schematic structural diagram of a printing apparatus for identifying a 3D model type according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of a printing apparatus that recognizes a type of a 3D model in a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It is to be further noted that, for the convenience of description, only a part of the structure relating to the present invention is shown in the drawings, not the whole structure.
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the steps as a sequential process, many of the steps can be performed in parallel, concurrently or simultaneously. In addition, the order of the steps may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. Processing may correspond to methods, functions, procedures, subroutines, and the like.
Furthermore, the terms "first," "second," and the like may be used herein to describe various orientations, actions, steps, elements, or the like, but the orientations, actions, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, a first module may be termed a second module, and, similarly, a second module may be termed a first module, without departing from the scope of the present application. The first module and the second module are both modules, but they are not the same module. The terms "first", "second", etc. are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Example one
Fig. 1 is a flow diagram illustrating a printing method for identifying a 3D model type according to an embodiment of the present invention, where the printing method for identifying a 3D model type according to an embodiment of the present invention is suitable for a case where a 3D model is identified through artificial intelligence before printing and parameter configuration is performed, and specifically, the printing method for identifying a 3D model type according to an embodiment of the present invention includes:
and step 100, acquiring the imported 3D model.
In this embodiment, the principle of the 3D printing technology is: firstly, a 3D digital model is established on a computer, then "printing materials" such as powder or colloid are loaded into a printer, the printer is connected with the computer, 3D digital model data in the computer are read, the movement and material output of a printing nozzle are controlled, the "printing materials" are laminated and added in a layer, and finally, a blueprint on the computer is changed into a real object. The 3D model is generally a model created by 3D software in 3D printing, and printing can be performed by inputting the model into the 3D printing software. Typically, three-dimensional model construction is performed by computer-aided design (CAD) software or other three-dimensional modeling software. The model can be designed independently and can also be obtained by scanning a real object. For example, for a small-scale rock sample, high-precision micro-nano CT scanning is adopted to obtain an internal structure image of the natural rock, and then a three-dimensional reconstruction method is utilized to establish a digital three-dimensional structure network model.
And step 110, acquiring a plurality of screenshot photos according to the 3D model.
In this embodiment, the obtaining of the multiple screenshot photos according to the 3D model includes: and shooting from multiple directions according to the 3D model to obtain multiple screenshot pictures. Specifically, multi-directional screenshots are performed on the 3D model obtained in step 100 to obtain multiple 3D model screenshots in different directions. In this embodiment, the taking from multiple directions according to the 3D model to obtain multiple screenshot photos includes: shooting from the front, the side and the back according to the 3D model to obtain a plurality of screenshot pictures, carrying out screenshot snapshot on the model from the front, the side and the back at three different angles, and shooting and intercepting the 3D model in multiple directions, so that the 3D model type can be accurately identified by the deep learning model. In an alternative embodiment, multiple pictures can be taken from multiple different directions, for example, the upper left corner, the lower left corner, the top surface and the like are multidirectional, and when more screenshot pictures are taken, the recognition accuracy is higher, so that the method is more suitable for the deep learning model to recognize, and the recognition accuracy is improved.
And 120, inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type.
In this embodiment, the deep learning model is a software function module that enables a machine to have an analysis learning capability like a human and to recognize data such as characters, images, and sounds, and in this embodiment, the deep learning model may be a neural network model or a convolutional neural network model, and in this embodiment, inputting a plurality of screenshot photos into a pre-trained deep learning model outputs a recognition result, where the recognition result is a 3D model type corresponding to the plurality of screenshot photos, exemplarily, inputting the plurality of screenshot photos into the pre-trained deep learning model, and the deep learning model outputs the 3D model type after recognition, such as an automobile model, a toy model, a handheld model, and so on.
And step 130, acquiring corresponding parameter configuration according to the matched 3D model type and printing.
In this embodiment, the 3D model type matched in step 120 is obtained, and ingredients are configured and input according to parameters corresponding to the 3D model, for example, printing materials used by the automobile model and the handheld model are different, and it is generally considered in the prior art that material proportioning is performed manually after the printing model is determined, but in this embodiment, the 3D model type identified by the deep learning model is input with the ingredients corresponding to the 3D model type for printing, specifically, many materials usable for 3D printing are available, for example, the material used for SLA is liquid photosensitive resin, the LOM uses paper, a metal film, a plastic film, and the SLS uses thermoplastic, metal powder, and ceramic powder. After the printed material is determined, an appropriate 3D printer is selected. Currently, 3D printers are classified into two categories, industrial grade and desktop grade (consumer grade). The more well-known companies include 3D Systems, Stratasys, Formlabs, Hewlett packard, EOS, Israel Objet. In this embodiment, the specific printing manner is not limited in this embodiment.
The printing method for identifying the type of the 3D model provided by the embodiment of the invention comprises the following steps: obtaining an imported 3D model; acquiring a plurality of screenshot photos according to the 3D model; inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type; and acquiring corresponding parameter configuration according to the matched 3D model type and printing. The type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting is carried out through manual judgment in the prior art is solved, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are achieved.
Example two
Fig. 2 is a flow diagram illustrating a printing method for identifying a 3D model type according to a second embodiment of the present invention, which is further explained and supplemented with respect to part of the first embodiment, and this embodiment is suitable for a case where a 3D model is identified through artificial intelligence before printing and parameter configuration is performed, and specifically, the printing method for identifying a 3D model type according to the second embodiment of the present invention includes:
and 200, acquiring parameter configurations matched with the various 3D models.
And 210, establishing a database according to the multiple 3D models and the parameter configuration matched with the multiple 3D models.
In this embodiment, before performing 3D model identification, a plurality of 3D models are first matched with their corresponding parameter configurations one by one, for example, the printed materials, the printing machines, etc. corresponding to the automobile model are different from those corresponding to the handheld model, and thus different 3D models need to be uniformly matched with their corresponding parameter configurations and then input into the database, and thereafter, the identified deep learning model records may also be saved in data, which is convenient for saving and storing historical data, and facilitates fast speed ratio comparison and query of the model and its corresponding parameter configuration.
And step 220, acquiring the imported 3D model.
And step 230, acquiring a plurality of screenshot photos according to the 3D model.
And 240, training the untrained deep learning model to obtain a trained deep learning model.
In this embodiment, a suitable deep learning model, for example, a convolutional neural network model or a neural network model, is first selected, specifically, the model type is not limited in this embodiment, and after the suitable deep learning model is selected, the model needs to be trained through a large amount of sample data to obtain the trained deep learning model.
Referring to fig. 3, in the present embodiment, step 240 further includes:
and 241, acquiring a sample screenshot photo and a 3D model type matched with the sample screenshot photo.
And 242, training the untrained deep learning model according to the sample screenshot picture and the 3D model type matched with the sample screenshot picture to obtain the trained deep learning model.
And step 250, inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type.
In this embodiment, a depth learning model that is not trained is trained by obtaining a plurality of sample images, where the plurality of sample images are 3D model types in which a plurality of screenshot photographs are matched with the sample images, for example, an automobile model is matched with a plurality of screenshot photographs in each direction to form one sample image, the screenshot in each direction is used as an input, the automobile model is used as an output, and the untrained depth learning model is trained. The acquisition of the sample images can be performed in a multi-direction screenshot mode on the determined 3D model, so that a plurality of screenshot sheets are acquired, in order to enable the deep learning model to be more accurate, more sample images are preferably selected as much as possible for training, and therefore the training precision is improved.
And step 260, acquiring corresponding parameter configuration according to the matched 3D model type and printing.
In this embodiment, referring to fig. 4, step 260 further includes:
and 261, acquiring and setting corresponding parameter configuration according to the matched 3D model.
And 262, carrying out slice printing according to the set 3D model.
In this embodiment, after the parameters of the 3D model are configured, the model is divided into slices in the computer, the printing path and the parameters are set, and the 3D model is transmitted to the 3D printer to print the three-dimensional digital model into a solid body. And after printing and forming, carrying out post-processing on the real object according to the requirement of the printer to obtain a 3D printing entity.
The printing method for identifying the type of the 3D model provided by the embodiment of the invention comprises the following steps: acquiring parameter configurations matched with the various 3D models and the various 3D models; establishing a database according to the multiple 3D models and the parameter configuration matched with the multiple 3D models; acquiring an imported 3D model; acquiring a plurality of screenshot photos according to the 3D model; training an untrained deep learning model to obtain a trained deep learning model; inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type; and acquiring corresponding parameter configuration according to the matched 3D model type and printing. The type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting is carried out through manual judgment in the prior art is solved, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are achieved.
EXAMPLE III
Fig. 5 is a diagram illustrating a printing apparatus for identifying a 3D model type according to a third embodiment of the present invention, where the printing apparatus for identifying a 3D model type according to the third embodiment of the present invention can execute a printing method for identifying a 3D model type according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 5, the printing apparatus 300 for recognizing a type of a 3D model includes:
a model obtaining module 310, configured to obtain an imported 3D model;
a picture acquiring module 320, configured to acquire multiple screenshot pictures according to the 3D model;
the model matching module 330 is configured to input the multiple screenshot photos into a pre-trained deep learning model to obtain a matched 3D model type;
and the model printing module 340 is configured to obtain corresponding parameter configuration according to the matched 3D model type and perform printing.
Optionally, in an embodiment, the method further includes:
the parameter matching module is used for acquiring parameter configurations of various 3D models and matching of the various 3D models;
and the parameter acquisition module is used for establishing a database according to the multiple 3D models and the parameter configuration matched with the multiple 3D models.
Optionally, in an embodiment, the method further includes:
and the picture acquisition sub-module is used for shooting from multiple directions according to the 3D model so as to acquire multiple screenshot pictures.
Optionally, in an embodiment, the method further includes:
a picture acquisition unit: the system is used for shooting from the front, the side and the back according to the 3D model to obtain a plurality of screenshot photos.
Optionally, in an embodiment, the method further includes:
and the model training module is used for training the untrained deep learning model to obtain the trained deep learning model.
Optionally, in an embodiment, the method further includes:
the sample acquisition sub-module is used for acquiring a sample screenshot photo and a 3D model type matched with the sample screenshot photo;
and the model training module is used for training the untrained deep learning model according to the sample screenshot picture and the 3D model type matched with the sample screenshot picture so as to obtain the trained deep learning model.
Optionally, in an embodiment, the method further includes:
the parameter setting module is used for acquiring and setting corresponding parameter configuration according to the matched 3D model;
and the slice printing module is used for printing slices according to the set 3D model.
The embodiment provides a printing apparatus for recognizing a type of a 3D model, including: the model acquisition module is used for acquiring the imported 3D model; the picture acquisition module is used for acquiring a plurality of screenshot pictures according to the 3D model; the model matching module is used for inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type; and the model printing module is used for acquiring corresponding parameter configuration according to the matched 3D model type and printing. The imported 3D model type is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting is carried out through manual judgment in the prior art is solved, and the effects of improving the printing precision and simplifying the printing process of the automatic identification model are achieved.
Example four
Fig. 6 is a schematic structural diagram of a printing apparatus 12 for identifying a type of a 3D model according to a fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of an exemplary recognition 3D model type printing device 12 suitable for use in implementing embodiments of the present invention. The printing apparatus 12 for identifying the type of the 3D model shown in fig. 6 is only an example, and should not bring any limitation to the function and the range of use of the embodiment of the present invention.
As shown in FIG. 6, the printing device 12 that identifies the type of 3D model is in the form of a general purpose computing device. The components of printing device 12 that identify the 3D model type may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. Printing device 12, which recognizes the type of 3D model, may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by running a program stored in the system memory 28, for example, implementing a file processing method applied to 3D printing provided by an embodiment of the present invention:
acquiring an imported 3D model;
acquiring a plurality of screenshot photos according to the 3D model;
inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and acquiring corresponding parameter configuration according to the matched 3D model type and printing.
The printing equipment for identifying the type of the 3D model is used for executing the following method: acquiring an imported 3D model; acquiring a plurality of screenshot photos according to the 3D model; inputting the multiple picture-cutting pictures into a pre-trained deep learning model to obtain a matched 3D model type; and acquiring corresponding parameter configuration according to the matched 3D model type and printing. The type of the imported 3D model is identified through artificial intelligence, so that the optimal printing parameters and relevant settings are automatically matched, the problem that parameter setting is carried out through manual judgment in the prior art is solved, and the effects of automatically identifying the model, improving the printing precision and simplifying the printing process are achieved.
EXAMPLE five
Fifth, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the printing method for identifying a type of a 3D model according to any embodiment of the present invention:
acquiring an imported 3D model;
acquiring a plurality of screenshot photos according to the 3D model;
inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and acquiring corresponding parameter configuration according to the matched 3D model type and printing.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions without departing from the scope of the invention. Therefore, although the present invention has been described in more detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A printing method for identifying a type of a 3D model, comprising:
acquiring an imported 3D model;
acquiring a plurality of screenshot photos according to the 3D model;
inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and acquiring corresponding parameter configuration according to the matched 3D model type and printing.
2. The printing method for identifying a type of a 3D model according to claim 1, wherein the obtaining of the imported 3D model further comprises:
acquiring parameter configurations matched with the various 3D models and the various 3D models;
and establishing a database according to the multiple 3D models and the parameter configuration matched with the multiple 3D models.
3. The printing method for identifying the type of 3D model according to claim 1, wherein the obtaining a plurality of screenshot photos according to the 3D model comprises:
and shooting from multiple directions according to the 3D model to obtain multiple screenshot pictures.
4. A printing method for identifying a type of a 3D model according to claim 3, wherein said taking from a plurality of directions to obtain a plurality of picture-shots according to the 3D model comprises:
and shooting from the front, the side and the back according to the 3D model to obtain a plurality of screenshot photos.
5. The printing method for identifying a 3D model type according to claim 1, wherein the step of obtaining a plurality of screenshot photos according to the 3D model type further comprises:
and training the untrained deep learning model to obtain the trained deep learning model.
6. The printing method for recognizing the type of the 3D model according to claim 5, wherein the training the untrained deep learning model to obtain the trained deep learning model comprises:
obtaining a sample screenshot photo and a 3D model type matched with the sample screenshot photo;
and training the untrained deep learning model according to the sample screenshot picture and the 3D model type matched with the sample screenshot picture to obtain the trained deep learning model.
7. The printing method for identifying the type of the 3D model according to claim 1, wherein the obtaining and printing the corresponding parameter configuration according to the matched 3D model comprises:
acquiring corresponding parameter configuration according to the matched 3D model and setting;
and carrying out slice printing according to the set 3D model.
8. A printing apparatus that identifies a type of a 3D model, comprising:
the model acquisition module is used for acquiring the imported 3D model;
the picture acquisition module is used for acquiring a plurality of screenshot pictures according to the 3D model;
the model matching module is used for inputting the multiple screenshot pictures into a pre-trained deep learning model to obtain a matched 3D model type;
and the model printing module is used for acquiring corresponding parameter configuration according to the matched 3D model type and printing.
9. A printing device that identifies a type of 3D model, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the printing method of identifying a type of 3D model as claimed in any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the printing method for identifying a type of a 3D model according to any one of claims 1-7.
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| US20200250322A1 (en) * | 2017-10-27 | 2020-08-06 | Hewlett-Packard Development Company, L.P. | Three-dimensional (3d) model protection via consumables |
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